Stop Chasing the Latest AI Model
Every couple of months, there's a new frontier model release. Benchmarks go up, Twitter (X) gets excited, and you start wondering if you should switch your entire workflow.
I've been there. In 2025, I used ChatGPT, Gemini and Claude and other models in Windsurf, Cursor and Cline. I tried designing UIs on v0, Lovable, Bolt.new and Replit.
Here's my advice for 2026: pick an AI model/agent and stick with it. The real productivity gains come from building your system around it, not from hopping between models. When you spend time configuring your environment, writing custom instructions, and developing repeatable processes, you create something that compounds over time. Every new feature or project benefits from the lessons of the last one.
Switching to a new model usually means switching agents and starting over. Your carefully crafted guides and prompts might not work the same way. Your mental model of how the AI "thinks" needs recalibrating. You lose momentum.
I use Claude Code with Opus 4.5, and over the past year I've built up a system that works for me. The improvements I've seen aren't just because Claude got smarter (Opus 4.5 has made a huge jump). They're because I got better at working with Claude Code. Here's what I have done:
Project-Level Instructions
I maintain a CLAUDE.md file in every project. It contains context about the codebase, conventions I follow, and things Claude should know before touching any code. When I start a new project, I copy over my base template and adapt it.
This file evolves with the project. After some back-and-forth with Claude about architecture or how to approach a task, I ask it to add the lessons learned to the file.
Skills for Common Patterns
Skills are reusable instruction sets for tasks I do regularly. Here are two examples from my setup:
Design skill: Before building any UI, Claude checks my ui directory to see which shadcn components I've already installed. It then analyses what the feature needs and looks up additional components from a curated list of providers via the shadcn registry. Only after this audit does it start designing. The goal is to avoid reinventing components that already exist.
Laravel scaffold skill: When I need a new feature, Claude follows a documented workflow for creating the migration, model, data object, controller, and Inertia page with typed props. It knows my conventions for where files go and how they're structured.
These skills mean I don't repeat myself across sessions. I point Claude to the relevant file and it picks up the context.
Hooks for Repetitive Tasks
Claude Code supports hooks that run before or after certain actions. I use these to enforce linting, run tests automatically, and format code. The AI doesn't need to remember to do these things because they happen regardless. I also use the Stop hook to notify me when its work is complete.
Domain-Specific Context
For Laravel projects, I use Laravel Boost, a first-party package that gives Claude deep understanding of my codebase. It provides specialised tools that let Claude inspect my database schema, run Artisan commands, inspect error logs, and search documentation for Laravel and other packages. Instead of me searching the docs and pasting snippets into prompts, Claude can look things up itself. This has resulted in more accurate code generation with fewer back-and-forth corrections.
None of these things are complicated on their own. But together, I spend less time explaining and more time building.
This is the real advantage of committing to a single coding agent and model. You stop chasing the best models and start optimising for the best workflow. And workflows, unlike benchmarks, are something you control.
So in 2026, resist the urge to chase every new frontier model. Build your system, refine it, and let it work for you.
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